Source code for pytorch_lightning.callbacks.prediction_writer

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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Aids in saving predictions
from typing import Any, Optional, Sequence

import pytorch_lightning as pl
from pytorch_lightning.callbacks.callback import Callback
from pytorch_lightning.utilities import LightningEnum
from pytorch_lightning.utilities.exceptions import MisconfigurationException

class WriteInterval(LightningEnum):
    BATCH = "batch"
    EPOCH = "epoch"
    BATCH_AND_EPOCH = "batch_and_epoch"

    def on_batch(self) -> bool:
        return self in (self.BATCH, self.BATCH_AND_EPOCH)

    def on_epoch(self) -> bool:
        return self in (self.EPOCH, self.BATCH_AND_EPOCH)

[docs]class BasePredictionWriter(Callback): """Base class to implement how the predictions should be stored. Args: write_interval: When to write. Example:: import torch from pytorch_lightning.callbacks import BasePredictionWriter class CustomWriter(BasePredictionWriter): def __init__(self, output_dir: str, write_interval: str): super().__init__(write_interval) self.output_dir = output_dir def write_on_batch_end( self, trainer, pl_module: 'LightningModule', prediction: Any, batch_indices: List[int], batch: Any, batch_idx: int, dataloader_idx: int ):, os.path.join(self.output_dir, dataloader_idx, f"{batch_idx}.pt")) def write_on_epoch_end( self, trainer, pl_module: 'LightningModule', predictions: List[Any], batch_indices: List[Any] ):, os.path.join(self.output_dir, "")) """ def __init__(self, write_interval: str = "batch") -> None: if write_interval not in list(WriteInterval): raise MisconfigurationException(f"`write_interval` should be one of {[i.value for i in WriteInterval]}.") self.interval = WriteInterval(write_interval)
[docs] def write_on_batch_end( self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", prediction: Any, batch_indices: Optional[Sequence[int]], batch: Any, batch_idx: int, dataloader_idx: int, ) -> None: """Override with the logic to write a single batch.""" raise NotImplementedError()
[docs] def write_on_epoch_end( self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", predictions: Sequence[Any], batch_indices: Optional[Sequence[Any]], ) -> None: """Override with the logic to write all batches.""" raise NotImplementedError()
[docs] def on_predict_batch_end( self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int, ) -> None: if not self.interval.on_batch: return batch_indices = trainer.predict_loop.epoch_loop.current_batch_indices self.write_on_batch_end(trainer, pl_module, outputs, batch_indices, batch, batch_idx, dataloader_idx)
[docs] def on_predict_epoch_end( self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: Sequence[Any] ) -> None: if not self.interval.on_epoch: return epoch_batch_indices = trainer.predict_loop.epoch_batch_indices self.write_on_epoch_end(trainer, pl_module, trainer.predict_loop.predictions, epoch_batch_indices)

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